Humans can identify new object classes from a small number of examples. However, most machine learning techniques require thousands of samples to produce comparable results.

Over the past ten years, computer vision researchers have mainly used millions of images to solve general problems. But this has led to a strong link between the amount of data and how well a model works. To solve this problem, researchers came up with Few-Shot Learning. It is on training models with fewer data without hurting how well they work.

Meta-learning

The most common few-shot learning algorithm uses meta-learning, also known as learning to learn. Meta-learning, a branch of machine learning, is driven by human development theory and focuses on learning priors from past experiences that can facilitate efficient downstream learning of new tasks. For instance, a simple learner only understands how to complete a single classification task, whereas a meta-learner learns how to achieve multiple related classification tasks. Consequently, the meta-learner could meet a similar but new job more quickly and effectively than a simple learner without prior experience completing the task.

A meta-learning procedure generally involves learning both within and across tasks on two levels. For instance, learning to classify accurately within a specific dataset is an example of rapid learning occurring within a job. The knowledge acquired gradually across tasks, which captures how task structure differs across target domains, is then used to guide this learning. Comparable strategies like transfer, multitask, and ensemble learning can vary from meta-learning. 

Transfer learning

In the transfer learning method, a model is trained on a single task, referred to as the source task, in the source domain where there is enough training data. This trained model is then further improved or retrained on the target task, which is a single task within the target domain. Knowledge is from the source task to the target task. Therefore, the more alike the two parts are, the better it functions. Learning multiple lessons at once is known as multitasking. It begins with no prior knowledge and makes an effort to maximize efficiency when handling several tasks concurrently.

However, multiple models, such as classifiers or experts, are strategically generated and combined to solve a specific problem through the ensemble learning process. In contrast, a meta-learner gathers experience by completing several related tasks before applying that knowledge to new situations. These methods can, and frequently are, combined in a meaningful way with meta-learning systems.

Significance

• Humans can distinguish between different handwritten characters after seeing a few examples, which serves as a test base for learning like a human. However, for computers to categorize what they "see" and distinguish between handwritten characters, they need a lot of data. In the few-shot learning test, computers are supposed to learn from a small number of examples, just like people do.
• Machine learning, for rare cases, uses few-shot learning. For instance, a machine learning model trained using few-shot learning techniques can correctly classify an image of a rare species after only being exposed to a small amount of prior knowledge when classifying images of animals.
• Few-shot learning eliminates the high costs associated with data collection and labelling because it uses fewer data to train a model. 

Applications

Computer vision

Few-shot learning is primarily utilized in computer vision to address issues like:

• character identification
• classification of images
• Object identification
• gesture identification

NLP

Natural language processing (NLP) applications can complete tasks with little text data using few-shot learning.

Audio Processing

Acoustic signal processing can analyze data containing information about voices or sounds, and few-shot learning enables the deployment of tasks like voice cloning and conversion.

Robotics

Robots need to be able to extrapolate knowledge from a few examples to act more human-like. Therefore, few-shot learning is essential for teaching robots to perform specific tasks like:

• imitating a single demonstration to pick up a movement
• learning manipulation techniques by watching a few examples
• continuous visual navigation control